Olfa Belkahla Driss

Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model

Display Omitted Hybrid metaheuristics is proposed to schedule machines and transport robots.A genetic algorithm is applied by a scheduler agent to explore the search space.A local search is used by cluster agents to guide the search in promising regions.A new disjunctive graph is presented to model simultaneously this problem.Computational results are presented using three sets of benchmark instances. In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be processed on a set of alternative machines and additionally have to be transported between them by several transport robots. Hence, the FJSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the flexible job shop scheduling problem and the robot routing problem. This paper proposes hybrid metaheuristics based on clustered holonic multiagent model for the FJSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using three sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review

The Job Shop scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Job Shop scheduling Problem with Transportation resources (JSPT) is a generalization of the classical JSP consisting of two sub-problems: the job scheduling problem and the generic vehicle scheduling problem. In this paper, we make a state-of-the-art review of the different works proposed for the JSPT, where we present a new classification schema according to seven criteria such as the transportation resource number, the transportation resource type, the job complexity, the routing flexibility, the recirculation constraint, the optimization criteria and the implemented approaches.

Reachability search in timed Petri nets using constraint programming

This paper presents a logical abstraction of the reachability graph of a timed Petri net using constraint programming. We apply it to the scheduling of transient inter-production states for cyclic productions in flexible manufacturing system. So, we propose to adapt the approach of Benasser and Yim (1999) based on the search of the accessibility by means of constraints using concepts of partial marking and partial step which allow a logical abstraction of the reachability graph of a Petri net. Having the timed Petri net (where a duration is associated to each transition), we propagate time to the obtained steps. In fact, we associate, to each marking extracted from a step, a timestamp vector: each timestamp corresponds to the date of the last token produced in a place at a step. Then, under temporal constraints, we solve scheduling problems, using constraint programming

Minimizing makespan in multi-factory flow shop problem using a chemical reaction metaheuristic

Solving scheduling problems is an important branch of operational research field. It consists in allocation number of jobs to machines taking into consideration a set of constraints. Recently, a new generalization of the permutation flow shop scheduling problem with multi-factory environment has been proposed. In this work, we suggest to apply a Chemical Reaction Optimization (CRO) metaheuristic to solve this problem in order to minimize makespan or the total duration of the schedule. An experimental study is carried out in order to analyze the performance of the proposed algorithm and to compare it with powerful algorithms on standard benchmarks.

Hybrid Metaheuristics within a Holonic Multiagent Model for the Flexible Job Shop Problem

Abstract The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) that allows to process operations on one machine out of a set of alternative machines. It is an NP-hard problem consisting of two sub-problems which are the assignment and the scheduling problems. This paper proposes a hybridization of two metaheuristics within a holonic multiagent model for the FJSP. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a cluster agents set uses a local search technique to guide the research in promising regions. Numerical tests are made to evaluate our approach, based on two sets of benchmark instances from the literature of the FJSP, which are the Brandimarte and Hurink data. The experimental results show the efficiency of our approach in comparison to other approaches.

Multi Agent Model Based on Chemical Reaction Optimization for Flexible Job Shop Problem

The Flexible Job Shop Problem (FJSP) is an extension of classical job shop problem such that each operation can be processed on different machine and its processing time depends on the machine used. This paper proposes a new multi-agent model based on the meta-heuristic Chemical Reaction Optimization (CRO) to solve the FJSP in order to minimize the maximum completion time (makespan). Experiments are performed on benchmark instances proposed in the literature to evaluate the performance of our model.

Multi-Agent Model based on Tabu Search for the Permutation Flow Shop Scheduling Problem

The objective of this work is to present a distributed approach for the problem of finding a minimum makespan in the permutation flow shop scheduling problem. The problem is strongly NP-hard and its resolution optimally within a reasonable time is impossible. For these reasons we opted for a Multi-agent architecture based on cooperative behaviour allied with the Tabu Search meta-heuristic. The proposed model is composed of two classes of agents: Supervisor agent, responsible for generating the initial solution and containing the Tabu Search core, and Scheduler agents which are responsible for the satisfaction of the constraints under their jurisdiction and the evaluation of all the neighborhood solutions generated by the Supervisor agent. The proposed approach has been tested on different benchmarks data sets and results demonstrate that it reaches high-quality solutions.

A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion

Abstract The Permutation Flowshop Scheduling Problem (PFSP) is among the most investigated scheduling problems in the fields of Operational Research (OR) and management science. During the last six decades, it has gained much attention and interest thanks to its applicability in a variety of domains such as industrial engineering and economics. Recently, the PFSP with multi-factory environment has been proposed in shop scheduling sphere. Since the problem is known to be NP-hard, exact algorithms can be extremely costly, computationally speaking. Chemical Reaction Optimization (CRO) is lastly proposed by Lam and Li (2010) to optimize hard combinatorial problems. Due to its ability to escape from local optima, CRO has demonstrated excellent performance in solving a variety of scheduling problems, such as flexible job-shop scheduling, grid scheduling, network scheduling etc. In such a paper, we address the Distributed Permutation Flowshop Scheduling Problem (DPFSP) with an artificial chemical reaction metaheuristic which objective is to minimize the maximum completion time. In the proposed CRO, the effective NEH heuristic is adapted to generate the initial population of molecules. Furthermore, a well-designed One-Point (OP) crossover and an effective greedy strategy are embedded in the CRO algorithm in order to ameliorate the solution quality. Moreover, the influence of the parameter setting on the CRO algorithm is being investigated on the base of the Taguchi method. To validate the performance of the proposed algorithm, intensive experiments are carried out on 720 large instances which are extended from the well known Taillard benchmark. The results prove the efficiency of the proposed algorithm in comparison with some powerful algorithms. It is also seen that more than 200 best-known solutions are improved.

A Modified Ant Colony Optimization algorithm for the Distributed Job shop Scheduling Problem

Abstract The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. The objective is to minimize the global makespan over all the factories. This paper is a first step to deal with the DJSP using three versions of a bio-inspired algorithm, namely the Ant Colony Optimization (ACO) which are the Ant System (AS), the Ant Colony System (ACS) and a Modified Ant Colony Optimization (MACO) aiming to explore more search space and thus guarantee better resolution of the problem. Comprehensive experiments are conducted to evaluate the performance of the three algorithms and the results show that the MACO is effective for the problem and AS and ACS algorithms in resolving the DJSP.